Electromagnetic (EM) scattering calculation is a very important part of most synthetic aperture radar (SAR) target image simulation methods. It affects the intensity of the radar echo signal to a great extent, thus affecting the quality of the final simulation image. EM reflection models are usually approximate formulas derived under certain assumptions. The errors between these models and the actual situation can cause significant differences between simulated images and real images. To solve this problem, we propose a novel modified SAR target image simulation framework, in which the deep neural network (DNN) is embedded to calculate the EM reflection, so that the DNN can directly learn and fit the EM reflection models from real SAR images. First, the intensity calculation of radar signal in a single reflection is separated from the cumulative calculation of multiple radar reflection signals intensity in each pixel. Thus, the approximate calculation formulas of EM reflection can be replaced with the DNN models. Next, the DNN model is trained with the backpropagation algorithm to learn the actual EM reflection model from real SAR images. Finally, the fitted EM reflection models and an image post-processing model are applied to simulate images of the target under different imaging angles. In the simulation framework, the functions of the neural network models are limited to calculating the reflection coefficient and adding sidelobe and speckle noise. The imaging model is still the original simulation method based on ray tracing, which ensures the correctness and generalization of the simulation method. Experiments show that the proposed simulation method can significantly improve the quality of the simulation image. When the image is normalized to [0, 1], the minimum mean square error between the simulated SAR images and the real images of the Sandia laboratory implementation of cylinders target can reach 0.003. The visualization results of the DNN models show that the fitted reflection coefficient calculation curve and the convolution kernel used for image post-processing are consistent with the laws in the theoretical model. In addition, when the proposed method is used to simulate complex targets, the similarity of simulation images can also be significantly improved.

中文翻译：

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嵌入DNN的SAR目标图像模拟计算电磁反射的方法。

电磁（EM）散射计算是大多数合成孔径雷达（SAR）目标图像模拟方法中非常重要的一部分。它在很大程度上影响雷达回波信号的强度，从而影响最终仿真图像的质量。EM反射模型通常是在某些假设下得出的近似公式。这些模型与实际情况之间的误差可能会导致模拟图像与真实图像之间的显着差异。为解决这一问题，我们提出了一种新颖的改进的SAR目标图像仿真框架，该框架中嵌入了深度神经网络（DNN）以计算EM反射，从而DNN可以直接从实际SAR图像中学习并拟合EM反射模型。 。第一的，一次反射中雷达信号强度的计算与每个像素中多次雷达反射信号强度的累加计算是分开的。因此，可以用DNN模型代替EM反射的近似计算公式。接下来，使用反向传播算法训练DNN模型，以从实际SAR图像中学习实际的EM反射模型。最后，将拟合的EM反射模型和图像后处理模型应用于模拟不同成像角度下目标的图像。在仿真框架中，神经网络模型的功能仅限于计算反射系数以及增加旁瓣和斑点噪声。成像模型仍然是基于光线跟踪的原始模拟方法，这保证了仿真方法的正确性和推广性。实验表明，所提出的仿真方法可以显着提高仿真图像的质量。当将图像标准化为[0，1]时，模拟的SAR图像和Sandia实验室实现的圆柱目标的真实图像之间的最小均方误差可以达到0.003。DNN模型的可视化结果表明，拟合后的反射系数计算曲线和用于图像后处理的卷积核与理论模型中的定律一致。另外，当将所提出的方法用于模拟复杂目标时，模拟图像的相似度也可以得到显着提高。实验表明，所提出的仿真方法可以显着提高仿真图像的质量。当将图像标准化为[0，1]时，模拟的SAR图像和Sandia实验室实现的圆柱目标的真实图像之间的最小均方误差可以达到0.003。DNN模型的可视化结果表明，拟合后的反射系数计算曲线和用于图像后处理的卷积核与理论模型中的定律一致。另外，当该方法用于模拟复杂目标时，模拟图像的相似度也可以得到显着提高。实验表明，所提出的仿真方法可以显着提高仿真图像的质量。当将图像标准化为[0，1]时，模拟的SAR图像和Sandia实验室实现的圆柱目标的真实图像之间的最小均方误差可以达到0.003。DNN模型的可视化结果表明，拟合后的反射系数计算曲线和用于图像后处理的卷积核与理论模型中的定律一致。另外，当该方法用于模拟复杂目标时，模拟图像的相似度也可以得到显着提高。气缸目标的桑迪亚实验室实现的模拟SAR图像和真实图像之间的最小均方误差可以达到0.003。DNN模型的可视化结果表明，拟合后的反射系数计算曲线和用于图像后处理的卷积核与理论模型中的定律一致。另外，当该方法用于模拟复杂目标时，模拟图像的相似度也可以得到显着提高。气缸目标的桑迪亚实验室实现的模拟SAR图像和真实图像之间的最小均方误差可以达到0.003。DNN模型的可视化结果表明，拟合后的反射系数计算曲线和用于图像后处理的卷积核与理论模型中的定律一致。另外，当将所提出的方法用于模拟复杂目标时，模拟图像的相似度也可以得到显着提高。